1 00:00:02,960 --> 00:00:12,600 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Bloomberg Intelligence 2 00:00:12,680 --> 00:00:14,400 Speaker 1: with Alex Steel and Paul Sweeney. 3 00:00:14,520 --> 00:00:17,760 Speaker 2: The real app performance has been the US corporate high yield. 4 00:00:17,920 --> 00:00:20,280 Speaker 3: Are the companies lean enough? Have they trimmed all the fats? 5 00:00:20,320 --> 00:00:24,119 Speaker 2: The semiconductor business is a really cyclical business. 6 00:00:23,640 --> 00:00:27,200 Speaker 1: Breaking market headlines and corporate news from across the globe. 7 00:00:27,280 --> 00:00:29,880 Speaker 3: Do investors like the M and A that we've seen? 8 00:00:30,120 --> 00:00:33,159 Speaker 2: These are two big time blue chip companies. 9 00:00:33,320 --> 00:00:36,920 Speaker 3: The window between the peak and cut changing super fast. 10 00:00:37,120 --> 00:00:42,040 Speaker 1: Bloomberg Intelligence with Alex Steel and Paul Sweeneye on Bloomberg Radio. 11 00:00:43,600 --> 00:00:45,839 Speaker 4: I'm Paul Sweeney and I'm Noram Melinda filling in for 12 00:00:45,960 --> 00:00:46,920 Speaker 4: Alex Steele. 13 00:00:46,720 --> 00:00:49,520 Speaker 2: On Today's Bloomberg Intelligence Show. We dig inside the big 14 00:00:49,560 --> 00:00:51,800 Speaker 2: business stories Impactney Wall Street and the global markets. 15 00:00:51,920 --> 00:00:54,240 Speaker 4: Each and every week we provide in depth research and 16 00:00:54,320 --> 00:00:56,880 Speaker 4: data on some of the two thousand companies and one 17 00:00:57,000 --> 00:00:59,800 Speaker 4: hundred and thirty industries are analysts cover worldwide. 18 00:01:00,000 --> 00:01:02,280 Speaker 2: Today, we'll look at how the most volatile quarter since 19 00:01:02,320 --> 00:01:04,800 Speaker 2: the heights of the pandemic has delivered a windfall to 20 00:01:04,840 --> 00:01:05,880 Speaker 2: Wall Streets trading desk. 21 00:01:06,080 --> 00:01:09,800 Speaker 4: Plus, we'll discuss how Volkswagon and Mercedes are being impacted 22 00:01:09,959 --> 00:01:11,720 Speaker 4: by electric vehicles in China. 23 00:01:11,760 --> 00:01:14,440 Speaker 2: But first we look at ASML, one of the world's 24 00:01:14,520 --> 00:01:18,160 Speaker 2: leading suppliers for the semiconductor industry. ASML shares plunged the 25 00:01:18,160 --> 00:01:20,760 Speaker 2: most in twenty six years after the company reported weak 26 00:01:20,880 --> 00:01:23,160 Speaker 2: orders last quarter for its chip making machines. 27 00:01:23,360 --> 00:01:26,479 Speaker 4: ASML also lowered its guidance for next year, and this 28 00:01:26,600 --> 00:01:29,360 Speaker 4: is now forcing investors to reevaluate the health of the 29 00:01:29,360 --> 00:01:30,600 Speaker 4: semiconductor industry. 30 00:01:30,800 --> 00:01:32,600 Speaker 2: For more on this, co host Alex Steele and I 31 00:01:32,640 --> 00:01:36,240 Speaker 2: were joined by Mandeep Sing, Bloomberg Intelligence senior tech industry analyst. 32 00:01:36,520 --> 00:01:38,920 Speaker 2: We first asked man Deep if this is a negative 33 00:01:38,959 --> 00:01:40,600 Speaker 2: sign for AI chip demand. 34 00:01:40,920 --> 00:01:45,240 Speaker 5: I mean, look, when it comes to ASML, again, everything 35 00:01:45,319 --> 00:01:48,800 Speaker 5: is driven by CAPEX, and when I say CAPEX, it's 36 00:01:48,920 --> 00:01:53,120 Speaker 5: really coming from the foundry guys like TSMC, like Samsung. 37 00:01:53,640 --> 00:01:57,480 Speaker 5: We know Samsung had layoffs recently, so clearly they are 38 00:01:57,520 --> 00:02:01,240 Speaker 5: not doing value well. But TSMC is so the fact 39 00:02:01,320 --> 00:02:06,559 Speaker 5: that their buyers are so concentrated and the geopolitical tensions continue, 40 00:02:06,760 --> 00:02:11,480 Speaker 5: and you know the second half estimates the comps are tougher. 41 00:02:11,600 --> 00:02:16,239 Speaker 5: To me, this is just a sign of expectations kind 42 00:02:16,280 --> 00:02:18,960 Speaker 5: of going up to the point where you will not 43 00:02:19,040 --> 00:02:22,120 Speaker 5: see any positive revisions from the print this quarter. And 44 00:02:22,160 --> 00:02:25,640 Speaker 5: that's what happened with Expectations had gone up and they 45 00:02:25,680 --> 00:02:28,560 Speaker 5: didn't surprise to the upside, so we could expect the 46 00:02:28,560 --> 00:02:29,440 Speaker 5: same from others. 47 00:02:29,600 --> 00:02:31,800 Speaker 2: The magnitude of the miss on the orders versus the 48 00:02:31,919 --> 00:02:35,920 Speaker 2: estimate seemed huge to me. Yes, that typical or not 49 00:02:36,040 --> 00:02:38,720 Speaker 2: to play. When they missed, they really miss well. 50 00:02:38,800 --> 00:02:43,320 Speaker 5: So's that comes down to how semis typically is when 51 00:02:43,360 --> 00:02:46,200 Speaker 5: you go back to prior cycles. When these companies miss, 52 00:02:46,320 --> 00:02:48,640 Speaker 5: they miss big. We have seen that with Micron, we 53 00:02:48,680 --> 00:02:51,080 Speaker 5: have seen that with some of the other names. But 54 00:02:51,240 --> 00:02:55,000 Speaker 5: in the case of a SML, the secular drivers are intact. 55 00:02:55,040 --> 00:02:58,040 Speaker 5: When you think about, you know, every foundry looking to 56 00:02:58,200 --> 00:03:01,400 Speaker 5: use their machines, looking to go to you know, smaller 57 00:03:01,480 --> 00:03:05,240 Speaker 5: nodes TSMC so they are their largest customer. And when 58 00:03:05,280 --> 00:03:08,440 Speaker 5: you think about, you know, how well TSMC has done 59 00:03:08,480 --> 00:03:11,400 Speaker 5: in terms of their AI revenue. I don't think they 60 00:03:11,440 --> 00:03:15,320 Speaker 5: are cutting back capex, but it's always about that incremental 61 00:03:15,360 --> 00:03:18,560 Speaker 5: buyer when it comes to these semi companies, especially the 62 00:03:18,560 --> 00:03:21,360 Speaker 5: ones that are reliant on CAPEX spend. And if you 63 00:03:21,440 --> 00:03:24,040 Speaker 5: take China out of the equation or the fact that 64 00:03:24,080 --> 00:03:27,040 Speaker 5: they are restricted in some way. Those are some of 65 00:03:27,080 --> 00:03:28,320 Speaker 5: your incremental buyers. 66 00:03:28,360 --> 00:03:31,040 Speaker 6: This is a really ignorant question. Where does in Nvidia 67 00:03:31,120 --> 00:03:33,239 Speaker 6: sit in this story that we're talking about. 68 00:03:33,160 --> 00:03:36,880 Speaker 5: Well, Nvidia is sort of the first derivative. So if 69 00:03:36,920 --> 00:03:41,000 Speaker 5: TSMC is not buying machines from ASML, that means they 70 00:03:41,000 --> 00:03:44,840 Speaker 5: are not expanding their supply for you know, the latest 71 00:03:44,840 --> 00:03:48,320 Speaker 5: cost packaging and the foundry side in terms of manufacturing 72 00:03:48,480 --> 00:03:53,800 Speaker 5: Nvidia's chips. So TSMC determines what kind of capacity expansion 73 00:03:53,840 --> 00:03:57,120 Speaker 5: they're looking for for twenty twenty five and beyond, and 74 00:03:57,240 --> 00:03:59,880 Speaker 5: based on that they are placing an order for a 75 00:04:00,080 --> 00:04:04,240 Speaker 5: SML equipment. So it is a very big sign. And 76 00:04:04,280 --> 00:04:06,880 Speaker 5: to your point about Intel being a buyer, well, Intel 77 00:04:06,920 --> 00:04:09,800 Speaker 5: is under pressure to curtail their capex as well, so 78 00:04:10,040 --> 00:04:12,880 Speaker 5: you're taking a lot of the incremental buyers out of 79 00:04:12,880 --> 00:04:16,240 Speaker 5: the equation even though there is no substitute for ASML. 80 00:04:16,320 --> 00:04:18,680 Speaker 5: So it's not as if ASML is losing market share 81 00:04:18,760 --> 00:04:22,479 Speaker 5: to anyone. It's just the incremental buyers are fewer compared 82 00:04:22,480 --> 00:04:24,440 Speaker 5: to where they were. You know, a couple of quarters back. 83 00:04:24,600 --> 00:04:27,080 Speaker 2: Okay, what I know about AI you can put into 84 00:04:27,120 --> 00:04:30,120 Speaker 2: a shot class. So answer this question like, I'm a 85 00:04:30,120 --> 00:04:33,560 Speaker 2: five year old. Is this fundamentally change the AI story 86 00:04:33,920 --> 00:04:34,360 Speaker 2: for tech? 87 00:04:35,200 --> 00:04:39,279 Speaker 5: It doesn't. It's just that everyone is expecting some sort 88 00:04:39,279 --> 00:04:42,040 Speaker 5: of digestion period when it comes to AI. We have 89 00:04:42,240 --> 00:04:45,640 Speaker 5: had you know, a long up to the right sort 90 00:04:45,680 --> 00:04:49,000 Speaker 5: of scenario so far. When it comes to generative AI, 91 00:04:49,400 --> 00:04:53,279 Speaker 5: and everyone expects a pause at some point, their signs 92 00:04:53,320 --> 00:04:56,160 Speaker 5: are you know, in Vidia chip demand remains in say 93 00:04:56,200 --> 00:05:00,880 Speaker 5: siable despite the restrictions, but when it comes to you know, SEMIS, 94 00:05:00,920 --> 00:05:04,080 Speaker 5: the way it works is first your foundry guys are 95 00:05:04,120 --> 00:05:07,600 Speaker 5: gonna slow down their supply expansion. Then you know, and 96 00:05:07,760 --> 00:05:11,839 Speaker 5: video will see fewer beat and raises, and so there 97 00:05:11,880 --> 00:05:14,400 Speaker 5: is a derivative aspect to how it flows through. It 98 00:05:14,440 --> 00:05:17,120 Speaker 5: doesn't all happen in the same quarter, and to me 99 00:05:17,520 --> 00:05:20,760 Speaker 5: ESML missing is one of the first signs that you know, 100 00:05:21,400 --> 00:05:24,040 Speaker 5: things may be cooling down a little bit. It may 101 00:05:24,040 --> 00:05:27,320 Speaker 5: not get reflected in Video's quarter this time around, but 102 00:05:27,520 --> 00:05:30,479 Speaker 5: two quarters down the line and Vidia could get effected. 103 00:05:30,160 --> 00:05:32,880 Speaker 6: Which then also reads the question like which customer is 104 00:05:32,880 --> 00:05:35,839 Speaker 6: the problem for ASML, like what are their customer lists 105 00:05:35,880 --> 00:05:38,039 Speaker 6: are calling them saying like, guys, look, we don't really 106 00:05:38,040 --> 00:05:40,960 Speaker 6: need the equipment, like we know it may not be TSMCS, 107 00:05:41,000 --> 00:05:43,239 Speaker 6: then is an Intel because that's more of an idiosyncratic 108 00:05:43,520 --> 00:05:47,320 Speaker 6: Intel issue rather than like a broader AI chip story issue. 109 00:05:47,520 --> 00:05:51,200 Speaker 5: Yeah, and Intel and Samsung, I mean, look, Samsung, we 110 00:05:51,360 --> 00:05:54,800 Speaker 5: know isn't doing very well on the manufacturing side. When 111 00:05:55,120 --> 00:05:59,040 Speaker 5: people talk about generative AI chips and GPUs, everyone is 112 00:05:59,080 --> 00:06:02,480 Speaker 5: going to MC as if there's only game in town. 113 00:06:02,839 --> 00:06:06,159 Speaker 5: Samsung isn't able to switch to that latest note for 114 00:06:06,320 --> 00:06:09,360 Speaker 5: you know, generative AI chips, and that's where we heard 115 00:06:09,400 --> 00:06:13,760 Speaker 5: Samsung doing a layoff. So clearly they are curtailing their costs. 116 00:06:14,040 --> 00:06:16,840 Speaker 5: Intel is curtailing their costs. So you take out two 117 00:06:16,880 --> 00:06:20,599 Speaker 5: of the top buyers of ASML gear and we know 118 00:06:20,880 --> 00:06:24,960 Speaker 5: they sell you know, multimillion dollar machines, So these are 119 00:06:25,080 --> 00:06:29,760 Speaker 5: expensive purchases. And it's not as if ASML is losing business, 120 00:06:29,839 --> 00:06:32,239 Speaker 5: it's just it won't get reflected in the next quarter 121 00:06:32,440 --> 00:06:33,760 Speaker 5: or you know, the couple of quarters. 122 00:06:33,880 --> 00:06:38,120 Speaker 4: Our thanks, saman Deep saying Bloomberg Intelligence senior tech industry analysts. 123 00:06:38,320 --> 00:06:41,080 Speaker 2: Each week we look at research from Bloomberg and EF 124 00:06:41,240 --> 00:06:43,200 Speaker 2: previously known as New Energy Finance. 125 00:06:43,320 --> 00:06:45,760 Speaker 4: They're the team at Bloomberg that tracks and analyzes the 126 00:06:45,920 --> 00:06:49,880 Speaker 4: energy transition from commodities to power, transport, industries, buildings, and 127 00:06:49,920 --> 00:06:52,680 Speaker 4: agriculture sectors. This week, we take a look at how 128 00:06:52,680 --> 00:06:55,920 Speaker 4: corporations are on pace to purchase record clean energy in 129 00:06:55,960 --> 00:06:56,800 Speaker 4: twenty twenty four. 130 00:06:57,080 --> 00:06:58,960 Speaker 2: For more on this, co host Alex Steele and I 131 00:06:59,000 --> 00:07:03,080 Speaker 2: were joined by Kyle prison bnef's head of sustainability research 132 00:07:03,440 --> 00:07:05,040 Speaker 2: first to ask Kyle to take a look at the 133 00:07:05,120 --> 00:07:08,479 Speaker 2: kind of corporations that are signing green power purchase agreements. 134 00:07:08,880 --> 00:07:12,920 Speaker 7: So they're locking into long term contracts for typically solar windpower, 135 00:07:12,960 --> 00:07:15,200 Speaker 7: but we're now seeing them expand into other forms of 136 00:07:15,280 --> 00:07:20,120 Speaker 7: low carbon technology for example, like nuclear, like hydro and geothermal. 137 00:07:20,760 --> 00:07:23,400 Speaker 7: Big technology companies have really led in this space. So 138 00:07:23,480 --> 00:07:28,480 Speaker 7: it's the companies like Amazon, Meta, Google, Microsoft, they're signing 139 00:07:28,520 --> 00:07:30,640 Speaker 7: the most deals at a large scale. But we're seeing 140 00:07:30,640 --> 00:07:33,320 Speaker 7: a lot of heavy emitting, hard to abate sectors getting 141 00:07:33,320 --> 00:07:37,200 Speaker 7: into this space now, so materials companies, industrials, oil and 142 00:07:37,240 --> 00:07:40,200 Speaker 7: gas companies. They're starting to break into new markets where 143 00:07:40,200 --> 00:07:42,640 Speaker 7: big tech maybe doesn't have as big of a footprint, 144 00:07:42,680 --> 00:07:45,360 Speaker 7: and they're starting to sign these long term, large scale 145 00:07:45,400 --> 00:07:46,280 Speaker 7: clean energy deals. 146 00:07:46,720 --> 00:07:49,800 Speaker 2: Where are the clean energy deals happening is it here 147 00:07:49,840 --> 00:07:51,720 Speaker 2: in the US, is it in Europe? Or where are 148 00:07:51,720 --> 00:07:52,800 Speaker 2: these things mostly happening? 149 00:07:53,040 --> 00:07:56,239 Speaker 7: Historically was the US, So between twenty fifteen and twenty 150 00:07:56,240 --> 00:07:59,760 Speaker 7: twenty two, around two thirds of all these corporate clean 151 00:07:59,840 --> 00:08:02,360 Speaker 7: up energy power purchase agreements were signed in the United States. 152 00:08:02,960 --> 00:08:05,320 Speaker 7: In twenty twenty three, that number drop to around forty 153 00:08:05,320 --> 00:08:09,119 Speaker 7: five percent. So corporations are increasingly spreading out and signing 154 00:08:09,160 --> 00:08:12,960 Speaker 7: deals in Europe, in Latin America and Southeast Asia, for example. 155 00:08:13,240 --> 00:08:15,520 Speaker 7: In Asia in particular, you have a lot of demand 156 00:08:15,560 --> 00:08:18,320 Speaker 7: for electricity from corporations and you have a huge supply 157 00:08:18,400 --> 00:08:21,280 Speaker 7: chain footprint, and historically those companies haven't been able to 158 00:08:21,520 --> 00:08:24,320 Speaker 7: buy clean energy. But through a lot of policy lobbying 159 00:08:24,360 --> 00:08:26,120 Speaker 7: and a lot of work on the ground with regulators, 160 00:08:26,440 --> 00:08:29,360 Speaker 7: you now have opportunities to buy clean energy. In Japan, 161 00:08:29,600 --> 00:08:32,320 Speaker 7: South Korea, Vietnam's a new market, so there's a lot 162 00:08:32,360 --> 00:08:33,560 Speaker 7: of new, exciting expansion. 163 00:08:34,280 --> 00:08:36,160 Speaker 3: What's the price for these things and how do they 164 00:08:36,160 --> 00:08:37,600 Speaker 3: compare it to traditional energy? 165 00:08:37,920 --> 00:08:39,720 Speaker 7: So that's been one of the biggest drivers in the 166 00:08:39,760 --> 00:08:43,000 Speaker 7: growth of this market right. So through September of this year, 167 00:08:43,080 --> 00:08:46,880 Speaker 7: companies have announced over thirty one gigawatts of clean energy 168 00:08:46,880 --> 00:08:49,560 Speaker 7: through corporate power purchase agreements. That's the size of a 169 00:08:49,559 --> 00:08:52,880 Speaker 7: small country in a given year, and we're on record pace. 170 00:08:52,920 --> 00:08:54,960 Speaker 7: And the biggest reason for that is that sol earned 171 00:08:55,000 --> 00:08:57,520 Speaker 7: wind on a new build basis are now cheaper than 172 00:08:57,559 --> 00:09:00,240 Speaker 7: colon gas in many markets around the world. So as 173 00:09:00,280 --> 00:09:03,040 Speaker 7: a corporate buyer, I can undercut those prices for power 174 00:09:03,320 --> 00:09:05,400 Speaker 7: that I might be paying, for example, from a utility 175 00:09:05,480 --> 00:09:07,840 Speaker 7: or from the grid by locking into a long term, 176 00:09:07,840 --> 00:09:10,479 Speaker 7: fixed contract for renewables and for renewables. 177 00:09:10,559 --> 00:09:13,160 Speaker 2: Is this is the adoption of renewables or the growth 178 00:09:13,160 --> 00:09:16,720 Speaker 2: of the renewables market. Is that driven by the market 179 00:09:16,920 --> 00:09:20,120 Speaker 2: or by regulations governments saying you gotta do this? 180 00:09:20,960 --> 00:09:23,200 Speaker 7: What do we learn I mean to the last question, 181 00:09:23,240 --> 00:09:26,800 Speaker 7: it's really primarily driven by economics. But reliability is a 182 00:09:26,840 --> 00:09:30,520 Speaker 7: huge factor here. So big technology companies they're now going out, 183 00:09:30,760 --> 00:09:33,360 Speaker 7: they're building these data centers. You're seeing a big expansion 184 00:09:33,400 --> 00:09:36,640 Speaker 7: in manufacturing capacity. All of this requires the lights to 185 00:09:36,679 --> 00:09:39,200 Speaker 7: be on twenty four to seven, right, So you can't 186 00:09:39,240 --> 00:09:41,920 Speaker 7: afford to have a power outage or a grid failure. 187 00:09:42,320 --> 00:09:44,960 Speaker 7: So locking into one of these contracts for solar and wind, 188 00:09:45,240 --> 00:09:48,080 Speaker 7: that gives you more reliability. And again that expansion into 189 00:09:48,160 --> 00:09:50,920 Speaker 7: other forms of what we would call zero carbon base 190 00:09:51,040 --> 00:09:53,520 Speaker 7: load power that could generate twenty four to seven, like 191 00:09:53,640 --> 00:09:57,200 Speaker 7: nuclear and geothermal, that also kind of emphasizes that reliability. 192 00:09:57,480 --> 00:09:59,720 Speaker 7: So it's a combo of that along with sustainability. 193 00:10:00,160 --> 00:10:02,640 Speaker 6: And how do you think that this sort of partnership 194 00:10:02,679 --> 00:10:04,800 Speaker 6: and evolution happens. It's still going to be these long 195 00:10:04,880 --> 00:10:06,840 Speaker 6: term power purchase agreements or is it going to be 196 00:10:06,880 --> 00:10:09,600 Speaker 6: like these hyper scalers And you know, maybe even like 197 00:10:09,600 --> 00:10:12,080 Speaker 6: a sman industry or the hard to debate industry just 198 00:10:12,120 --> 00:10:15,800 Speaker 6: sets up like their little small modular reactor right next 199 00:10:15,800 --> 00:10:18,760 Speaker 6: to their facility, or a wind farm right next to 200 00:10:18,800 --> 00:10:21,480 Speaker 6: their facility. I mean, I'm being hyperbole, but you get 201 00:10:21,480 --> 00:10:24,360 Speaker 6: the idea versus plugging into the grid for example. 202 00:10:24,679 --> 00:10:27,599 Speaker 7: You definitely need collaboration on the grid side, right, and 203 00:10:27,880 --> 00:10:30,600 Speaker 7: the utility scale side of this market. Of course, there 204 00:10:30,640 --> 00:10:34,120 Speaker 7: are opportunities to build a solar project or a wind 205 00:10:34,160 --> 00:10:37,320 Speaker 7: farm on site and leverage energy storage to get that 206 00:10:37,360 --> 00:10:40,680 Speaker 7: power directly, but we need utilities, right, and we need 207 00:10:40,720 --> 00:10:43,920 Speaker 7: grid planners to start collaborating with these corporate buyers to 208 00:10:44,040 --> 00:10:46,560 Speaker 7: ensure that this grid scale up is done sustainably. If 209 00:10:46,640 --> 00:10:49,319 Speaker 7: we start to build all these hyperscaler data centers in 210 00:10:49,679 --> 00:10:52,439 Speaker 7: for example, the Data Center Corridor in the eastern US 211 00:10:52,520 --> 00:10:55,240 Speaker 7: or in Texas. You need to ensure that there's enough 212 00:10:55,400 --> 00:10:58,560 Speaker 7: transmission capacity to ensure that that power gets moved from 213 00:10:58,600 --> 00:11:01,240 Speaker 7: A to B. Right, So that's to involve regulators, that 214 00:11:01,320 --> 00:11:03,960 Speaker 7: starts to involve utilities. So it really is kind of 215 00:11:03,960 --> 00:11:06,200 Speaker 7: an approach that everyone needs to be involved in for 216 00:11:06,240 --> 00:11:07,280 Speaker 7: this to be successful. 217 00:11:07,440 --> 00:11:10,520 Speaker 2: You know where they do wind farms in a big way, Ireland, 218 00:11:11,120 --> 00:11:12,800 Speaker 2: driving around tons of that. 219 00:11:13,200 --> 00:11:15,000 Speaker 7: A lot of data centers in Ireland as well, so 220 00:11:15,160 --> 00:11:16,280 Speaker 7: it's extra important there. 221 00:11:16,400 --> 00:11:19,199 Speaker 2: Yeah, so they were ever there, just big ones as well, 222 00:11:19,200 --> 00:11:21,440 Speaker 2: and it's windy there, so it works being an island 223 00:11:21,440 --> 00:11:23,840 Speaker 2: and all talk to just about you mentioned nuclear energy. 224 00:11:24,760 --> 00:11:27,120 Speaker 2: What's the future of nuclear here in this country? Can 225 00:11:27,200 --> 00:11:29,560 Speaker 2: we build these little nuclear plants that can do things 226 00:11:29,600 --> 00:11:30,680 Speaker 2: and not pose a big risk. 227 00:11:31,120 --> 00:11:34,079 Speaker 7: So I, unfortunately I can't comment too much on nuclear 228 00:11:34,280 --> 00:11:36,400 Speaker 7: We have an guy, we do have a nuclear guy. 229 00:11:37,480 --> 00:11:40,040 Speaker 7: I'll leave it to him, but what I would say 230 00:11:40,120 --> 00:11:42,440 Speaker 7: is right, there was a lot of noise around this 231 00:11:42,559 --> 00:11:46,000 Speaker 7: announcement from Microsoft around through my island. Through my island 232 00:11:46,040 --> 00:11:48,400 Speaker 7: is a name, right, that's a project that obviously evokes 233 00:11:48,400 --> 00:11:50,480 Speaker 7: a lot of emotion, but we're going to see a 234 00:11:50,480 --> 00:11:53,440 Speaker 7: lot of corporations continue to look for those deals with 235 00:11:53,559 --> 00:11:56,400 Speaker 7: that zero carbon based load power. So we wrote about 236 00:11:56,440 --> 00:11:58,640 Speaker 7: this the other week. This won't be the last nuclear 237 00:11:58,679 --> 00:12:01,360 Speaker 7: deal from big tech, right You'll see more geothermal deals. 238 00:12:01,720 --> 00:12:04,079 Speaker 7: So it's going to play a really important role here 239 00:12:04,120 --> 00:12:07,200 Speaker 7: as a reliable source of power that's also low carbon. 240 00:12:07,480 --> 00:12:11,000 Speaker 6: What region is signing the most of these clean PPAs 241 00:12:11,400 --> 00:12:11,760 Speaker 6: right now? 242 00:12:11,760 --> 00:12:14,800 Speaker 7: It's Texas and it's based purely on economics. So in 243 00:12:15,200 --> 00:12:18,480 Speaker 7: for example, northern and western Texas, the price of power 244 00:12:18,520 --> 00:12:22,199 Speaker 7: for wind is incredibly cheap and you have fantastic wind resources, 245 00:12:22,480 --> 00:12:23,960 Speaker 7: so you have a lot of companies going out and 246 00:12:24,000 --> 00:12:27,839 Speaker 7: signing deals there. But increasingly companies want to emphasize where 247 00:12:27,880 --> 00:12:30,640 Speaker 7: can they make the biggest impact by adding clean power. 248 00:12:30,880 --> 00:12:33,520 Speaker 7: If you already have all of this low carbon wind 249 00:12:33,559 --> 00:12:36,320 Speaker 7: and solar generating in Texas, are you really making that 250 00:12:36,400 --> 00:12:39,160 Speaker 7: much of a difference by adding another project there, for example, 251 00:12:39,200 --> 00:12:42,200 Speaker 7: compared to the eastern US where you have more coal power, 252 00:12:42,400 --> 00:12:44,720 Speaker 7: right where you can have a bigger impact on decarbonizing 253 00:12:44,720 --> 00:12:47,040 Speaker 7: the grid. So what we're going to slowly start to 254 00:12:47,080 --> 00:12:50,160 Speaker 7: see is more corporations expand that footprint, both to other 255 00:12:50,200 --> 00:12:52,960 Speaker 7: parts of the United States outside of Texas, but other 256 00:12:53,040 --> 00:12:54,400 Speaker 7: new regions in the world. 257 00:12:54,240 --> 00:12:57,400 Speaker 2: Are thanks to Kyle Howrison, bnef's head of sustainability research. 258 00:12:57,640 --> 00:12:59,800 Speaker 4: Coming up, we'll get how a slump in demand from 259 00:13:00,360 --> 00:13:03,480 Speaker 4: is impacting the luxury goods maker LVMH. 260 00:13:03,559 --> 00:13:06,360 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 261 00:13:06,360 --> 00:13:08,520 Speaker 2: depth research and data on two thousand companies and one 262 00:13:08,640 --> 00:13:11,600 Speaker 2: hundred and thirty industries. You can access Bloomberg Intelligence via 263 00:13:11,679 --> 00:13:12,920 Speaker 2: Bigo on the terminal. 264 00:13:12,960 --> 00:13:15,920 Speaker 4: I'm Paul Sweeney and I'm normal Linda. This is Bloomberg. 265 00:13:20,240 --> 00:13:24,120 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 266 00:13:24,200 --> 00:13:27,280 Speaker 1: weekdays at ten am Eastern on Affo card playing nbroud 267 00:13:27,320 --> 00:13:30,360 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 268 00:13:30,440 --> 00:13:33,600 Speaker 1: you get your podcasts, or watch us live on YouTube. 269 00:13:35,559 --> 00:13:38,440 Speaker 4: I'm Paul Swing and I'm Normalanda, filling in for Alex Steel. 270 00:13:38,720 --> 00:13:40,640 Speaker 2: We move now to US bank earnings. 271 00:13:40,760 --> 00:13:42,719 Speaker 4: The most fallat a quarter since the heights of the 272 00:13:42,800 --> 00:13:45,960 Speaker 4: pandemic has delivered a windfall to wall streets trading desks 273 00:13:46,160 --> 00:13:46,600 Speaker 4: last quarter. 274 00:13:46,679 --> 00:13:49,480 Speaker 2: Goldman Sachs Bank of America City Group and JP Morgan 275 00:13:49,559 --> 00:13:52,640 Speaker 2: Chase posted equities and fixed income trading halls that surpassed 276 00:13:52,640 --> 00:13:53,520 Speaker 2: analyst estimates. 277 00:13:53,720 --> 00:13:56,520 Speaker 4: This week, Morgan Stanley joined the party and reported better 278 00:13:56,559 --> 00:13:59,800 Speaker 4: than expected trading revenue, fueling a thirty two percent profits 279 00:14:00,240 --> 00:14:03,240 Speaker 4: last quarter. As a result, Morgan Stanley shares surged the 280 00:14:03,360 --> 00:14:04,360 Speaker 4: most in four years. 281 00:14:04,679 --> 00:14:07,240 Speaker 2: For More, we were joined by Alison Williams, Bloomberg Intelligence, 282 00:14:07,280 --> 00:14:10,160 Speaker 2: senior analysts for Global Banks. We first asked Allison to 283 00:14:10,280 --> 00:14:12,360 Speaker 2: break down Morgan Stanley's quarterly results. 284 00:14:12,760 --> 00:14:15,640 Speaker 8: I think the real number that's giving investors confidence in 285 00:14:15,679 --> 00:14:17,760 Speaker 8: our opinion, you know, is the wealth flows. 286 00:14:18,559 --> 00:14:21,560 Speaker 9: So we've had some uneven flows. 287 00:14:21,800 --> 00:14:24,760 Speaker 8: Last quarter was was relatively weak, and I think o 288 00:14:24,880 --> 00:14:28,080 Speaker 8: the rebound this quarter maybe instilling a little bit of confidence. 289 00:14:28,720 --> 00:14:33,640 Speaker 8: As you know, they are strong institutionally, but they have 290 00:14:33,880 --> 00:14:37,640 Speaker 8: shifted their business over time to this, uh more towards 291 00:14:37,680 --> 00:14:40,640 Speaker 8: the wealth business. Gorman had sort of put some aggressive 292 00:14:40,680 --> 00:14:43,040 Speaker 8: targets out there before handing over the rains its head 293 00:14:43,120 --> 00:14:47,720 Speaker 8: pick and so and so to be clear, they really, 294 00:14:48,480 --> 00:14:51,040 Speaker 8: you know, beat the numbers across the board. It was 295 00:14:51,160 --> 00:14:54,160 Speaker 8: led by the institutional business, the equities trading business, in 296 00:14:54,200 --> 00:14:59,240 Speaker 8: which they're relatively more skewed did very well, partly because 297 00:14:59,280 --> 00:15:02,080 Speaker 8: they are skewed that business, and they did outperform. They 298 00:15:02,160 --> 00:15:06,120 Speaker 8: had the best growth in trading and fees across the 299 00:15:06,200 --> 00:15:10,239 Speaker 8: big six US bank so upset across the board, benefiting 300 00:15:10,280 --> 00:15:13,480 Speaker 8: from their mix, benefiting from their performance. But the wealth 301 00:15:13,520 --> 00:15:16,640 Speaker 8: flows are really what the focus is for investors also 302 00:15:16,800 --> 00:15:22,160 Speaker 8: because stocks are aiding their asset values, aiding fees and 303 00:15:22,400 --> 00:15:26,040 Speaker 8: so that pre tax margin in the business. The other 304 00:15:26,160 --> 00:15:27,560 Speaker 8: key metrics also doing better. 305 00:15:28,160 --> 00:15:31,360 Speaker 3: Alison, how much can we expect the wealth assets to 306 00:15:31,640 --> 00:15:32,160 Speaker 3: keep growing? 307 00:15:32,640 --> 00:15:36,720 Speaker 8: So, you know, that is the multiple trillion dollar question. 308 00:15:36,920 --> 00:15:40,520 Speaker 8: That is one of the aggressive targets that was put 309 00:15:40,560 --> 00:15:43,720 Speaker 8: out there was to you know, sort of aggressively grow 310 00:15:43,800 --> 00:15:46,120 Speaker 8: this assets, and it did seem like there were some 311 00:15:46,240 --> 00:15:50,000 Speaker 8: pretty healthy market gains priced in. So the markets are 312 00:15:50,120 --> 00:15:55,840 Speaker 8: delivering on those gains certainly this year, and the flows 313 00:15:55,920 --> 00:15:58,960 Speaker 8: are also helping. But you know, we would want to 314 00:15:59,000 --> 00:16:03,520 Speaker 8: see a couple more quarters of evidence that you know, 315 00:16:03,600 --> 00:16:07,120 Speaker 8: they've really built the momentum there. We would expect Morgan's 316 00:16:07,160 --> 00:16:11,840 Speaker 8: family to have a strong quarter in wealth this quarter, 317 00:16:12,120 --> 00:16:16,080 Speaker 8: just where stocks are. I mean, the global market cap 318 00:16:17,160 --> 00:16:19,960 Speaker 8: reached a record high, according to Bloomberg data at the 319 00:16:20,080 --> 00:16:22,800 Speaker 8: end of the third quarter, and I would keep in 320 00:16:22,920 --> 00:16:27,800 Speaker 8: mind that the pricing of those wealth fees really relates 321 00:16:27,880 --> 00:16:29,760 Speaker 8: to the beginning of quarter value. 322 00:16:29,840 --> 00:16:32,720 Speaker 9: So that's helpful for the bank for the fourth quarter 323 00:16:32,840 --> 00:16:33,360 Speaker 9: as well. 324 00:16:33,520 --> 00:16:36,000 Speaker 8: But you know, to your question, outs, when we're thinking 325 00:16:36,000 --> 00:16:38,680 Speaker 8: about the overall franchise and we're thinking about the growth, 326 00:16:39,240 --> 00:16:41,960 Speaker 8: we should keep in mind that there is a big 327 00:16:42,080 --> 00:16:45,080 Speaker 8: talent to the business this year from markets, and to 328 00:16:45,200 --> 00:16:49,600 Speaker 8: some extent, the future growth is sowhat dependent on that. 329 00:16:50,240 --> 00:16:52,840 Speaker 2: It just seems Allison, as I kind of read your 330 00:16:52,920 --> 00:16:55,720 Speaker 2: research and yeah, I know you guys have break data 331 00:16:55,760 --> 00:16:58,160 Speaker 2: on market share across all business lines, it just seems 332 00:16:58,200 --> 00:17:01,360 Speaker 2: like we're going to Stanley, Golden, Sachs, Morgan, they're just 333 00:17:01,440 --> 00:17:04,240 Speaker 2: kind of running away from everybody else on the planet. 334 00:17:04,320 --> 00:17:06,640 Speaker 2: I mean, is that in fact the case. 335 00:17:06,800 --> 00:17:07,960 Speaker 9: They have been? 336 00:17:08,280 --> 00:17:11,320 Speaker 8: And you know, whenever we expect you know that like, okay, 337 00:17:11,840 --> 00:17:15,560 Speaker 8: who's gonna who's left that can you know, blow up 338 00:17:15,600 --> 00:17:19,040 Speaker 8: that's a technical term, if you will, and see some 339 00:17:19,119 --> 00:17:21,320 Speaker 8: more share to these banks who are kind of running 340 00:17:21,359 --> 00:17:24,760 Speaker 8: out of names. But if you think about, for example, 341 00:17:24,920 --> 00:17:29,480 Speaker 8: what's happening in prime brokerage, right, so a lot of 342 00:17:29,840 --> 00:17:36,120 Speaker 8: these larger institutional hedge funds that have you know, sort 343 00:17:36,160 --> 00:17:38,800 Speaker 8: of these multipod shops, et cetera. To the extent that 344 00:17:38,920 --> 00:17:43,200 Speaker 8: those bigger firms are gaining assets and doing better, that's 345 00:17:43,320 --> 00:17:48,280 Speaker 8: benefiting you know, the leaders Morgan, Stanley Goldman and JP Morgan, 346 00:17:48,359 --> 00:17:51,120 Speaker 8: and so as as the bigger clients get bigger, that's 347 00:17:51,400 --> 00:17:55,480 Speaker 8: helping those firms as well. Within the trading business. The 348 00:17:55,600 --> 00:17:57,560 Speaker 8: other thing I would point to is, you know, the 349 00:17:57,640 --> 00:18:01,800 Speaker 8: investments in technology. These companies made the investments in technology, 350 00:18:02,480 --> 00:18:04,960 Speaker 8: and that's also helping them to win share in the 351 00:18:05,040 --> 00:18:05,920 Speaker 8: trading businesses. 352 00:18:06,800 --> 00:18:09,439 Speaker 6: I'm gonna steal Paul's comment slash question saying what does 353 00:18:09,520 --> 00:18:12,040 Speaker 6: this all mean about the European banks And are the 354 00:18:12,160 --> 00:18:14,199 Speaker 6: US banks eating their lunch or is the lunch being 355 00:18:14,240 --> 00:18:14,800 Speaker 6: spread around? 356 00:18:15,240 --> 00:18:19,879 Speaker 9: So I think for this quarter, the pie is getting bigger. 357 00:18:20,080 --> 00:18:23,760 Speaker 8: So you know, in terms of whether eating lunch or pie, 358 00:18:24,800 --> 00:18:27,520 Speaker 8: you know, the US banks have been gaining share against 359 00:18:27,560 --> 00:18:31,399 Speaker 8: the Europeans for many many years. We think that does continue, 360 00:18:32,000 --> 00:18:34,680 Speaker 8: but we think the pie is also bigger this quarter. 361 00:18:35,240 --> 00:18:38,760 Speaker 8: And what we heard specifically was, you know, for example, 362 00:18:38,880 --> 00:18:42,480 Speaker 8: JP Morgan strength across regions, and what we heard from 363 00:18:42,480 --> 00:18:46,520 Speaker 8: these banks was strength across derivatives, prime and cash equity, 364 00:18:46,640 --> 00:18:49,520 Speaker 8: so it is really broad based. The other thing that 365 00:18:49,600 --> 00:18:53,119 Speaker 8: we think is notable for UBS in particular is the 366 00:18:53,200 --> 00:18:57,399 Speaker 8: strength in Asia. So not surprisingly we saw a pickup 367 00:18:57,440 --> 00:19:01,760 Speaker 8: in activity in Asia that really benefited the banks, specifically 368 00:19:01,840 --> 00:19:03,800 Speaker 8: called out by JP Morgan and Morgan Stanley. 369 00:19:03,880 --> 00:19:05,440 Speaker 9: We think that that is really going to be a 370 00:19:05,520 --> 00:19:06,520 Speaker 9: help to UBS. 371 00:19:07,280 --> 00:19:10,480 Speaker 2: So how about on the cost side, Alison, was there 372 00:19:10,480 --> 00:19:12,840 Speaker 2: any discussion about compensation? I feel like that's been less 373 00:19:12,960 --> 00:19:15,680 Speaker 2: of a discussion point is. I guess Wall Street compensation 374 00:19:15,720 --> 00:19:17,520 Speaker 2: has become a little skewed, a little bit more towards 375 00:19:17,560 --> 00:19:19,520 Speaker 2: the fixed and a little bit less on the variable. 376 00:19:19,760 --> 00:19:21,639 Speaker 9: Yeah, so there's that element of it. 377 00:19:21,760 --> 00:19:22,040 Speaker 10: Paul. 378 00:19:22,640 --> 00:19:24,200 Speaker 9: You know, the two things I would point to is 379 00:19:24,280 --> 00:19:25,720 Speaker 9: keep in mind compensation. 380 00:19:25,440 --> 00:19:28,560 Speaker 8: Is a cruel din a cruel through the first three quarters, 381 00:19:28,640 --> 00:19:32,280 Speaker 8: so to some extent it represents the business trends, and 382 00:19:32,400 --> 00:19:34,680 Speaker 8: to some extent it represents how they think the full 383 00:19:34,800 --> 00:19:40,000 Speaker 8: year we'll shake out. Secondly, you know, compensation for the 384 00:19:40,119 --> 00:19:44,600 Speaker 8: investment banking fee side of things sort of was stickier 385 00:19:44,720 --> 00:19:46,920 Speaker 8: than we would have expected on the downside, and I 386 00:19:46,960 --> 00:19:49,040 Speaker 8: think that's because there was such a scramble to Higher 387 00:19:49,119 --> 00:19:53,680 Speaker 8: Town in twenty twenty one. So and investment banks have 388 00:19:53,760 --> 00:19:57,000 Speaker 8: been talking about a recovery in that fee business for 389 00:19:57,359 --> 00:20:00,920 Speaker 8: several quarters now, and so I think they that COMP 390 00:20:01,000 --> 00:20:03,040 Speaker 8: didn't come down as much, So there might not be 391 00:20:03,200 --> 00:20:06,439 Speaker 8: as much of a search to the upside. But if 392 00:20:06,520 --> 00:20:09,639 Speaker 8: we looked at costs, if we looked at COMP, you know, 393 00:20:09,800 --> 00:20:12,719 Speaker 8: in general the profitability was good just because the upside 394 00:20:12,720 --> 00:20:15,680 Speaker 8: to revenue was so much. Now, is that because the 395 00:20:15,760 --> 00:20:18,600 Speaker 8: investors banks are keeping conservative and going to see how 396 00:20:18,640 --> 00:20:22,040 Speaker 8: the fourth quarter shakes out in terms of the accrual basis? 397 00:20:23,119 --> 00:20:26,000 Speaker 8: Is part of it because of the stickiness as I mentioned, 398 00:20:26,920 --> 00:20:30,880 Speaker 8: But you did see COMP coming in sort of above estimates, 399 00:20:31,240 --> 00:20:33,360 Speaker 8: just not as much as the revenue upside. 400 00:20:33,680 --> 00:20:36,960 Speaker 4: Our thanks to Allison Williams, Bloomberg Intelligence, Senior Analyst, Global 401 00:20:37,000 --> 00:20:38,240 Speaker 4: Banks and Asset Managers. 402 00:20:38,560 --> 00:20:42,040 Speaker 2: We move next to earnings from the luxury goods maker LVMH. 403 00:20:42,480 --> 00:20:45,520 Speaker 4: This week, shares of LVMH plunged for the company reported 404 00:20:45,560 --> 00:20:47,720 Speaker 4: that sales of fashion and leather goods fell for the 405 00:20:47,800 --> 00:20:50,680 Speaker 4: first time since the pandemic. The company cited a slump 406 00:20:50,760 --> 00:20:53,639 Speaker 4: in demand from once insatiable Chinese consumers. 407 00:20:53,960 --> 00:20:55,560 Speaker 2: For more on this co host Alex Steel and I 408 00:20:55,680 --> 00:20:58,920 Speaker 2: were joined by Deborah Aik and Bloomberg Intelligence Luxury goods 409 00:20:58,960 --> 00:21:02,480 Speaker 2: analysts were first asked to break down LVMH. 410 00:21:01,840 --> 00:21:05,720 Speaker 11: Earnings quite a mixed bag, but yeah, generally across the 411 00:21:05,800 --> 00:21:08,040 Speaker 11: board are a little bit of a shock in the 412 00:21:08,320 --> 00:21:10,520 Speaker 11: Q three to everyone, and we felt it would really 413 00:21:10,640 --> 00:21:14,560 Speaker 11: ripple through the industry with their organic sales down three 414 00:21:14,640 --> 00:21:17,560 Speaker 11: percent in the Q three and that was all when 415 00:21:17,600 --> 00:21:20,760 Speaker 11: we went reagion by region. This a small amount of 416 00:21:20,800 --> 00:21:25,240 Speaker 11: growth in Europe and in North America, but we have 417 00:21:25,520 --> 00:21:29,399 Speaker 11: decline in sales in China down mid single digit. And 418 00:21:29,480 --> 00:21:32,320 Speaker 11: what that does overall for nine month, it moves organic 419 00:21:32,400 --> 00:21:36,760 Speaker 11: sales grow flat year on year. So it's not about pricing, 420 00:21:37,080 --> 00:21:40,359 Speaker 11: it's about product volume is down around five percent, price 421 00:21:40,440 --> 00:21:41,520 Speaker 11: and mixer up slightly. 422 00:21:42,160 --> 00:21:44,560 Speaker 2: So I know, just from talking to you over the 423 00:21:44,640 --> 00:21:47,400 Speaker 2: year's deb and reading your research, China is really key 424 00:21:47,520 --> 00:21:51,320 Speaker 2: for this luxury market. What's happening there? Is it just 425 00:21:51,480 --> 00:21:54,479 Speaker 2: simply reflection of a you know, a tough economic environment there, 426 00:21:54,520 --> 00:21:55,439 Speaker 2: particularly for the consumer. 427 00:21:56,560 --> 00:21:59,520 Speaker 11: Yeah, I think if we look at consumer sentiment, it's 428 00:21:59,640 --> 00:22:06,560 Speaker 11: back to COVID twenty nineteen, twenty twenty lows, it's that bad, 429 00:22:06,760 --> 00:22:12,359 Speaker 11: and we waited for the financial stimulus package. Boomberg had 430 00:22:12,359 --> 00:22:15,480 Speaker 11: popped out a consensus seeking two hundred and eighty three 431 00:22:15,560 --> 00:22:19,320 Speaker 11: billion in terms of stimulus package into the marketplace. And 432 00:22:19,480 --> 00:22:25,240 Speaker 11: while they talked about supporting housing, residential housing and other 433 00:22:25,880 --> 00:22:29,119 Speaker 11: back in local government spreadsheets doing a lot more for 434 00:22:29,240 --> 00:22:32,160 Speaker 11: real estate, they just aren't any numbers yet in the marketplace. 435 00:22:32,680 --> 00:22:36,000 Speaker 11: And then through October overall the beginning of October, we've 436 00:22:36,040 --> 00:22:39,640 Speaker 11: had Golden Week and that's been really contrasted money spent 437 00:22:39,720 --> 00:22:44,560 Speaker 11: on experiences on food. In Shanghai, there have been vouchers 438 00:22:44,600 --> 00:22:47,200 Speaker 11: going out in the lower income areas in about one 439 00:22:47,280 --> 00:22:50,359 Speaker 11: in ten or ten percent of money coming in has 440 00:22:50,400 --> 00:22:53,200 Speaker 11: been via voucher for meals over that period of time, 441 00:22:53,560 --> 00:22:56,240 Speaker 11: but there's just not as much going into the shopping baskets. 442 00:22:57,200 --> 00:22:58,840 Speaker 2: How about the Great market I know that's always been 443 00:22:58,880 --> 00:23:02,800 Speaker 2: a challenge for the luxury brands in China, well around 444 00:23:02,840 --> 00:23:06,480 Speaker 2: the world, but particularly in China. Is that still a headwind? 445 00:23:07,440 --> 00:23:10,800 Speaker 11: The companies won't really, you know, talk so deeply about that, 446 00:23:10,960 --> 00:23:13,399 Speaker 11: but they do try as much as they can in 447 00:23:13,560 --> 00:23:17,600 Speaker 11: terms of managing that marketplace. I think though a lot 448 00:23:17,680 --> 00:23:21,120 Speaker 11: of the generation and what these companies are doing online 449 00:23:21,160 --> 00:23:25,439 Speaker 11: and with newness. There are products even within the Louiston 450 00:23:25,560 --> 00:23:29,600 Speaker 11: range like they're never full new style bag, which is reversible. 451 00:23:29,800 --> 00:23:32,040 Speaker 11: Those types of products are doing very well, whether it's 452 00:23:32,080 --> 00:23:35,800 Speaker 11: from an LVMH or another brand that's struggling, like Ferrogamo, 453 00:23:35,920 --> 00:23:38,520 Speaker 11: some of its new brands coming out it also reported 454 00:23:39,600 --> 00:23:41,159 Speaker 11: and you know some of the some of the products 455 00:23:41,200 --> 00:23:43,399 Speaker 11: coming out there are doing very well and resonating with 456 00:23:43,480 --> 00:23:47,600 Speaker 11: a younger generation. So they do want authenticity. They've just 457 00:23:47,720 --> 00:23:50,840 Speaker 11: been very much more aware in that middle range and 458 00:23:50,920 --> 00:23:53,760 Speaker 11: that are certainly feeding through on some of the portfolio 459 00:23:53,920 --> 00:23:57,879 Speaker 11: within even within you know, they're very wide and deeper 460 00:23:58,520 --> 00:24:01,200 Speaker 11: product range that Alvia mage As. I mean, they still 461 00:24:01,240 --> 00:24:03,359 Speaker 11: did sixty billion, but when you look at their numbers 462 00:24:03,359 --> 00:24:05,440 Speaker 11: and overall they were one billion off, that's how big 463 00:24:05,520 --> 00:24:05,800 Speaker 11: they are. 464 00:24:06,359 --> 00:24:08,880 Speaker 2: Okay, interesting, So if I'm a big you know these 465 00:24:08,960 --> 00:24:12,639 Speaker 2: luxury brands, European and American luxury brands, what's my strategy 466 00:24:13,280 --> 00:24:15,240 Speaker 2: in China? Do I just wait for the consumer to 467 00:24:15,640 --> 00:24:19,320 Speaker 2: turn around? Do I maybe pursue some discounting that I 468 00:24:19,480 --> 00:24:21,280 Speaker 2: historically would not do to move product? 469 00:24:21,359 --> 00:24:22,080 Speaker 10: What's the strategy? 470 00:24:23,080 --> 00:24:28,000 Speaker 11: I think neither of those. I think price is relatively 471 00:24:28,160 --> 00:24:34,479 Speaker 11: flat after two three years of heightened inflation. Costs are 472 00:24:34,600 --> 00:24:39,240 Speaker 11: under control generally, and what you do is you innovate. 473 00:24:39,920 --> 00:24:43,240 Speaker 11: You stay mindful and in the face of the consumer 474 00:24:43,280 --> 00:24:46,080 Speaker 11: in a very targeted way, and on the back of 475 00:24:46,160 --> 00:24:47,920 Speaker 11: that you have to have a new product coming through. 476 00:24:48,280 --> 00:24:52,520 Speaker 11: There's a huge amount of investment of cape spend in 477 00:24:52,840 --> 00:24:58,760 Speaker 11: supply chain, in distribution logistics online with third parties. There 478 00:24:58,840 --> 00:25:03,600 Speaker 11: are projects out there that are collaborative projects with local 479 00:25:04,400 --> 00:25:09,000 Speaker 11: A star celebrities. So you've been very mindful in your 480 00:25:09,160 --> 00:25:12,520 Speaker 11: ticking boxes in the biggest cities being there and also 481 00:25:12,680 --> 00:25:17,200 Speaker 11: being really relevant in events too. So you keep going 482 00:25:17,960 --> 00:25:20,320 Speaker 11: and new wait for the base our thanks. 483 00:25:20,160 --> 00:25:23,440 Speaker 2: To Debacon, Bloomberg Intelligence luxury goods analysts. 484 00:25:23,240 --> 00:25:25,439 Speaker 4: Coming up on the program a look into how climate 485 00:25:25,560 --> 00:25:28,040 Speaker 4: change is making parts of the planet unensurable. 486 00:25:28,240 --> 00:25:31,240 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 487 00:25:31,320 --> 00:25:33,560 Speaker 2: depth research and data on two thousand companies and one 488 00:25:33,640 --> 00:25:36,440 Speaker 2: hundred and thirty industries. You can access Bloomberg Intelligence via 489 00:25:36,480 --> 00:25:37,639 Speaker 2: b I go on the terminal. 490 00:25:37,720 --> 00:25:40,760 Speaker 4: I'm Paul Sweeney and I'm normal Linda. This is Bloomberg. 491 00:25:46,520 --> 00:25:50,359 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 492 00:25:50,480 --> 00:25:54,000 Speaker 1: weekdays at ten am Eastern on applecar Play and Android 493 00:25:54,040 --> 00:25:56,760 Speaker 1: Outo with the Bloomberg Business app. You can also listen 494 00:25:56,920 --> 00:25:59,880 Speaker 1: live on Amazon Alexa from our flagship New York State 495 00:26:00,400 --> 00:26:03,120 Speaker 1: Just Say Alexa playing Bloomberg eleven thirty. 496 00:26:04,680 --> 00:26:07,760 Speaker 4: I'm Paul Swinning and I'm Normalanda filling in for Alex Deel. 497 00:26:08,000 --> 00:26:10,080 Speaker 2: This week, we focused on a Bloomberg Big Takes story 498 00:26:10,280 --> 00:26:13,399 Speaker 2: titled Catastrophe Bonds Will Help Florida but Failed Jamaica. 499 00:26:13,760 --> 00:26:16,280 Speaker 4: You can find it on Bloomberg dot Com and The Terminal. 500 00:26:16,480 --> 00:26:19,119 Speaker 4: The story looks at the use of catastrophe bonds as 501 00:26:19,160 --> 00:26:22,119 Speaker 4: climate change makes parts of the planet unensurable, and it 502 00:26:22,240 --> 00:26:24,920 Speaker 4: examines how vulnerable nations can be left out of luck 503 00:26:25,080 --> 00:26:26,240 Speaker 4: when disaster strikes. 504 00:26:26,520 --> 00:26:28,160 Speaker 2: For more, co hosts Alex Steel and I were joined 505 00:26:28,160 --> 00:26:31,639 Speaker 2: by one of the story's authors, Gautam Nike, Bloomberg's ESG editor. 506 00:26:31,720 --> 00:26:33,960 Speaker 2: We first asked Gautam to walk us through the story. 507 00:26:34,440 --> 00:26:37,320 Speaker 10: So essentially, this particular story I've done with a couple 508 00:26:37,359 --> 00:26:41,720 Speaker 10: of colleagues looks at catastrophe bonds. These are a bet 509 00:26:41,840 --> 00:26:47,679 Speaker 10: on the probability of huge natural disasters like earthquakes, wildfires, floods, 510 00:26:47,720 --> 00:26:50,960 Speaker 10: are hurricanes, and obviously the incidents of some of these 511 00:26:51,440 --> 00:26:55,679 Speaker 10: events have scotten higher and the severity of some climate 512 00:26:55,800 --> 00:26:59,800 Speaker 10: related events like hurricanes and floods have become more intense. 513 00:27:00,480 --> 00:27:06,000 Speaker 10: So these catastrophe bonds are a way for insurance and 514 00:27:06,119 --> 00:27:10,120 Speaker 10: reinsurance companies to pass on the risk of growing disasters 515 00:27:10,640 --> 00:27:13,520 Speaker 10: onto Wall Street, onto the capital markets, and not to 516 00:27:13,560 --> 00:27:15,600 Speaker 10: put it on their own balance sheet. The way it 517 00:27:15,680 --> 00:27:17,480 Speaker 10: works is that if you're an investor in the bond, 518 00:27:18,000 --> 00:27:20,800 Speaker 10: you can make a very large return that could be 519 00:27:20,880 --> 00:27:25,080 Speaker 10: fined hard to find in you another fixed income product. However, 520 00:27:25,400 --> 00:27:29,040 Speaker 10: if the particularly disaster is defined in the bond does occur, 521 00:27:29,760 --> 00:27:32,320 Speaker 10: then you could lose some or even all of your 522 00:27:32,400 --> 00:27:35,240 Speaker 10: invested capitals. So it's a gamble on a weather disaster. 523 00:27:35,960 --> 00:27:39,520 Speaker 2: Very simply, do these work, Yes, they. 524 00:27:39,440 --> 00:27:43,119 Speaker 10: Absolutely do work. So just to give you the broader context, 525 00:27:43,760 --> 00:27:46,960 Speaker 10: about seventy percent of all catastrophe bonds are focused on 526 00:27:47,040 --> 00:27:51,240 Speaker 10: the US windstorm sector because hurricanes and other severe name storms, 527 00:27:51,560 --> 00:27:53,959 Speaker 10: and a big chunk of that relates to Florida. Obviously, 528 00:27:54,040 --> 00:27:57,440 Speaker 10: this came into focus recently because of Hurricanes Helene and 529 00:27:57,520 --> 00:27:59,760 Speaker 10: Milton back to back that caused a lot of flooding 530 00:27:59,800 --> 00:28:02,600 Speaker 10: and wind damage as well. And the way it works 531 00:28:02,640 --> 00:28:06,840 Speaker 10: is that if a particular threshold of losses is met 532 00:28:07,040 --> 00:28:11,119 Speaker 10: for most of these catastrophe bonds, then the issuing party 533 00:28:11,280 --> 00:28:14,680 Speaker 10: will get a good chunk of the money that's taken 534 00:28:14,760 --> 00:28:17,720 Speaker 10: out from the money that the capital markets of the 535 00:28:17,760 --> 00:28:19,920 Speaker 10: Wall Street investors put in when they bought the bond, 536 00:28:20,240 --> 00:28:22,840 Speaker 10: and that money then goes to pig to fix people's 537 00:28:22,960 --> 00:28:25,080 Speaker 10: roofs and you know, rebuilt homes. 538 00:28:25,920 --> 00:28:28,040 Speaker 6: When has it not worked? As I mentioned the title 539 00:28:28,240 --> 00:28:30,800 Speaker 6: was they helped Florida but failed Jamaic US. So what's 540 00:28:30,840 --> 00:28:32,480 Speaker 6: the scenario where they don't pay off? 541 00:28:33,359 --> 00:28:36,560 Speaker 10: Yeah, So catastrophe bonds have been around for about twenty 542 00:28:36,600 --> 00:28:39,280 Speaker 10: five to thirty years and they've largely developed, as I said, 543 00:28:39,320 --> 00:28:41,760 Speaker 10: in the US, but also in Europe and Japan against 544 00:28:41,800 --> 00:28:46,080 Speaker 10: earthquake risk. But increasingly institutions like the World Bank, the IMF, 545 00:28:46,200 --> 00:28:50,160 Speaker 10: the OECD are trying to popularize them in the developing world. Now, 546 00:28:50,240 --> 00:28:52,640 Speaker 10: this is a part of the globe that is being 547 00:28:52,720 --> 00:28:55,360 Speaker 10: disproportionately hit by a lot of climate losses to which 548 00:28:55,400 --> 00:28:57,800 Speaker 10: they're not directly linked because you know, most of the 549 00:28:57,880 --> 00:29:00,720 Speaker 10: emissions have come from since the Industrial Revolution in the 550 00:29:00,800 --> 00:29:03,080 Speaker 10: western parts of the world, but a lot of these 551 00:29:03,160 --> 00:29:08,440 Speaker 10: weather disasters are focused on southern hemisphere. So the World 552 00:29:08,480 --> 00:29:12,440 Speaker 10: Bank and other institutions are trying to get developing countries 553 00:29:12,480 --> 00:29:17,320 Speaker 10: that are facing these risks to issue these catastrophe bonds 554 00:29:17,360 --> 00:29:19,400 Speaker 10: that have been around in the West. But there is 555 00:29:19,480 --> 00:29:23,120 Speaker 10: a problem the way the bond is structured. In the West, 556 00:29:23,160 --> 00:29:25,920 Speaker 10: the ways it developed seems to work quite well, but 557 00:29:26,200 --> 00:29:30,800 Speaker 10: in a developing country, because they don't have an insurance market, 558 00:29:31,040 --> 00:29:37,360 Speaker 10: you can't actually calculate the total claims on an insured basis. 559 00:29:37,640 --> 00:29:41,520 Speaker 10: So they've come up with a different way of structuring 560 00:29:41,600 --> 00:29:45,920 Speaker 10: the bond. It's called a parametric approach. Basically, for a hurricane, 561 00:29:46,080 --> 00:29:49,400 Speaker 10: it would simply be if the pressure of the hurricane 562 00:29:49,680 --> 00:29:53,160 Speaker 10: hits a certain threshold, which indicates wind speed, then the 563 00:29:53,240 --> 00:29:56,640 Speaker 10: bond will pay out. But if it misses, you get nothing. 564 00:29:57,000 --> 00:29:59,920 Speaker 10: Even if you're missed by the tiniest fractions, the rule 565 00:30:00,240 --> 00:30:02,760 Speaker 10: say you will get zero, and that's sort of what 566 00:30:02,840 --> 00:30:03,720 Speaker 10: happened with Jamaica. 567 00:30:04,120 --> 00:30:06,000 Speaker 2: If you believe in climate change, and if you believe 568 00:30:06,080 --> 00:30:08,880 Speaker 2: that weathers can become more and more unstable, it might 569 00:30:08,920 --> 00:30:11,040 Speaker 2: be really hard to price the risk out here because 570 00:30:11,040 --> 00:30:12,719 Speaker 2: it seems like the risk would always be going up 571 00:30:12,760 --> 00:30:16,760 Speaker 2: of more and more again unstable weather and catastrophes here, 572 00:30:17,000 --> 00:30:18,280 Speaker 2: how does the market account. 573 00:30:17,960 --> 00:30:22,400 Speaker 10: For that, You've been pointed at problem. So when someone's 574 00:30:22,440 --> 00:30:24,600 Speaker 10: trying to calculate the risk of these kind of weather 575 00:30:24,800 --> 00:30:30,200 Speaker 10: related catastrophe bonds, all you have really is historical data. 576 00:30:30,360 --> 00:30:33,720 Speaker 10: So for hurricanes, you have one hundred and fifty hundred 577 00:30:33,720 --> 00:30:36,680 Speaker 10: and seventy years a pretty robust data going back, and 578 00:30:36,840 --> 00:30:42,240 Speaker 10: you can make a good estimate. But unfortunately the calculation 579 00:30:42,400 --> 00:30:45,800 Speaker 10: has been muddied now with climate change, and these forecasts 580 00:30:45,840 --> 00:30:48,480 Speaker 10: are of course forecasts is something that hasn't happened yet 581 00:30:48,520 --> 00:30:51,000 Speaker 10: that will project it to happen, but you just don't 582 00:30:51,080 --> 00:30:54,360 Speaker 10: know how it might unfold depending on, you know, how 583 00:30:54,400 --> 00:30:58,400 Speaker 10: the world reacts to increased CO two missions. So the 584 00:30:58,480 --> 00:31:01,800 Speaker 10: whole question is how do you incorporate the climate effect 585 00:31:02,120 --> 00:31:07,080 Speaker 10: into this historical data and provide a really reliable metric 586 00:31:07,320 --> 00:31:12,080 Speaker 10: for someone to make a financial bet on. And it's 587 00:31:12,160 --> 00:31:15,600 Speaker 10: the uncertainty of well, to some extent, the models that 588 00:31:15,680 --> 00:31:18,560 Speaker 10: are used there by no means perfect, but also this 589 00:31:19,080 --> 00:31:21,440 Speaker 10: extra new element that we've seen in the last few 590 00:31:21,480 --> 00:31:23,280 Speaker 10: decades of climate change. 591 00:31:24,120 --> 00:31:25,800 Speaker 6: So, you guys, as I mentioned in the beginning, this 592 00:31:25,920 --> 00:31:27,760 Speaker 6: is part three of a series that you guys have 593 00:31:27,880 --> 00:31:31,080 Speaker 6: done into how climate change is making the planet unensurable. 594 00:31:31,400 --> 00:31:32,920 Speaker 4: What is your key takeaway here? 595 00:31:34,880 --> 00:31:37,880 Speaker 10: I think one of the main takeaways would be that 596 00:31:38,240 --> 00:31:43,320 Speaker 10: it is very hard to accurately model the risk of 597 00:31:43,400 --> 00:31:47,240 Speaker 10: climate change, and of course their attempts being made to 598 00:31:47,320 --> 00:31:52,440 Speaker 10: refine it constantly. But if you're trying to ensure a 599 00:31:52,600 --> 00:31:55,600 Speaker 10: large proportion of the world that is not insured, in 600 00:31:55,720 --> 00:31:58,840 Speaker 10: the developing world, also parts of Europe. I mean, there's 601 00:31:58,840 --> 00:32:01,120 Speaker 10: a huge insurance protection and YAP in Europe, and even 602 00:32:01,160 --> 00:32:04,600 Speaker 10: in the US, for example, in Florida, you know, insurance 603 00:32:04,640 --> 00:32:08,120 Speaker 10: companies have moved out. They're not providing insurance in California 604 00:32:08,160 --> 00:32:10,840 Speaker 10: for earthquake risks and wildfi. They're moving out. So the 605 00:32:10,920 --> 00:32:15,640 Speaker 10: main takeaway is that it's becoming a more difficult problem, 606 00:32:15,920 --> 00:32:19,440 Speaker 10: it's becoming a more uninsurable planet because of climate risk. 607 00:32:19,920 --> 00:32:23,440 Speaker 4: Our thanks to Galatam Naike Bloomberg ESG editor. Another great 608 00:32:23,440 --> 00:32:25,880 Speaker 4: Bloomberg Big Take story we looked at focused on how 609 00:32:25,960 --> 00:32:28,320 Speaker 4: a lot of the auto industry is getting squeezed by 610 00:32:28,440 --> 00:32:29,920 Speaker 4: cheap electric vehicles from China. 611 00:32:30,120 --> 00:32:33,160 Speaker 2: The piece is entitled VW and Mercedes are getting left 612 00:32:33,200 --> 00:32:35,520 Speaker 2: in the dust by China's evs. From what on the 613 00:32:35,560 --> 00:32:37,760 Speaker 2: story costs. Alex Steele and I were joined by Oliver 614 00:32:37,920 --> 00:32:41,479 Speaker 2: Crook Bloomberg, your correspondent. We first asked Oliver, just how 615 00:32:41,560 --> 00:32:43,400 Speaker 2: far behind Mercedes and VWR? 616 00:32:43,880 --> 00:32:45,720 Speaker 12: It depends kind of where you want to approach this 617 00:32:45,840 --> 00:32:48,120 Speaker 12: question from when we talk about the Chinese market, which 618 00:32:48,160 --> 00:32:51,720 Speaker 12: again is a very key market for Mercedes, BMW and Volkswagen. 619 00:32:51,960 --> 00:32:54,040 Speaker 12: This is their biggest market, or has traditionally been their 620 00:32:54,080 --> 00:32:56,320 Speaker 12: biggest market for a very long time. You look at 621 00:32:56,400 --> 00:32:58,480 Speaker 12: just the three third quarter deliveries that we got just 622 00:32:58,520 --> 00:33:01,800 Speaker 12: a couple of weeks ago. BMW alone was down thirty 623 00:33:01,840 --> 00:33:03,640 Speaker 12: percent in terms of the sales that they were making 624 00:33:03,680 --> 00:33:05,440 Speaker 12: in China, and that is their biggest market. So that 625 00:33:05,560 --> 00:33:08,440 Speaker 12: illustrates the scale of the importance of this here. The 626 00:33:08,520 --> 00:33:10,320 Speaker 12: problem is is that this is not just a question 627 00:33:10,440 --> 00:33:12,360 Speaker 12: of the Chinese slowdown, right. This isn't a question of 628 00:33:12,440 --> 00:33:14,560 Speaker 12: just we're going to put some stimulus in. Demand's going 629 00:33:14,600 --> 00:33:17,280 Speaker 12: to come back. The nature of that demand in China 630 00:33:17,520 --> 00:33:19,800 Speaker 12: is now changing. So what we see now in the 631 00:33:19,880 --> 00:33:21,880 Speaker 12: Chinese market is that more than half of the car 632 00:33:22,000 --> 00:33:24,479 Speaker 12: is being sold, there are EV's and so while they 633 00:33:24,520 --> 00:33:26,680 Speaker 12: still retain a lot of these German car companies some 634 00:33:26,800 --> 00:33:28,680 Speaker 12: of that market share, a good amount of that market share, 635 00:33:28,880 --> 00:33:30,920 Speaker 12: it's really not in the EV section and they're not 636 00:33:31,000 --> 00:33:32,760 Speaker 12: even in the sort of top five or ten in 637 00:33:32,800 --> 00:33:35,640 Speaker 12: the leader board for evs. So the question is going forward, 638 00:33:35,760 --> 00:33:38,320 Speaker 12: how do you get competitive again? The problem they have 639 00:33:38,480 --> 00:33:41,120 Speaker 12: is they had that IC market share. It's about winning 640 00:33:41,240 --> 00:33:43,600 Speaker 12: that electric market share, and they're just way behind on 641 00:33:43,680 --> 00:33:45,200 Speaker 12: the inside of the car and the electronics. 642 00:33:45,320 --> 00:33:47,920 Speaker 6: Yeah, the IC is the internal combustion engine part, which 643 00:33:47,960 --> 00:33:50,160 Speaker 6: is the hardest part for these guys. 644 00:33:50,280 --> 00:33:53,520 Speaker 3: Is it actually the battery, Is it just doing it 645 00:33:53,600 --> 00:33:54,200 Speaker 3: all cheaply? 646 00:33:54,880 --> 00:33:56,200 Speaker 6: Or is it the bending in the metal? 647 00:33:56,280 --> 00:33:56,960 Speaker 9: For the evs? 648 00:33:57,040 --> 00:33:58,000 Speaker 3: Where is the struggle? 649 00:33:58,520 --> 00:34:00,280 Speaker 12: I mean, it's a combination of all of these things. 650 00:34:00,320 --> 00:34:02,600 Speaker 12: There is, of course, are the battery components, even the 651 00:34:02,680 --> 00:34:04,520 Speaker 12: sort of the evs that are sold here in Europe, 652 00:34:04,560 --> 00:34:06,280 Speaker 12: even the ones that are produced here in Europe. The 653 00:34:06,320 --> 00:34:08,880 Speaker 12: batteries are still coming from China. All of the components 654 00:34:08,920 --> 00:34:11,239 Speaker 12: are coming over from China because they've really sort of 655 00:34:11,360 --> 00:34:14,279 Speaker 12: been very proactive with their raw material policy. Everything from 656 00:34:14,320 --> 00:34:17,239 Speaker 12: the lithium to this processing that all goes on to China. 657 00:34:17,320 --> 00:34:19,160 Speaker 12: The other issues, of course, the cost base, and we're 658 00:34:19,200 --> 00:34:21,719 Speaker 12: starting to see that hit here in Europe where Volkswagen 659 00:34:21,880 --> 00:34:24,600 Speaker 12: is talking about closing factories for the very first time 660 00:34:24,719 --> 00:34:27,280 Speaker 12: in its history. They're talking about closing a factory in Belgium, 661 00:34:27,560 --> 00:34:30,080 Speaker 12: closing a few factories in Germany. And to understand why 662 00:34:30,160 --> 00:34:32,280 Speaker 12: that's so important, this is a company that has literally 663 00:34:32,480 --> 00:34:35,040 Speaker 12: never done that before in Europe, in large part because 664 00:34:35,160 --> 00:34:37,400 Speaker 12: you know, their supervisory board half of their seats are 665 00:34:37,440 --> 00:34:40,000 Speaker 12: held by sort of union representatives. So if they are 666 00:34:40,160 --> 00:34:42,880 Speaker 12: moving forward and making that decision, it's because things are very, 667 00:34:43,000 --> 00:34:45,000 Speaker 12: very challenging the cost base. If you look at the 668 00:34:45,080 --> 00:34:47,719 Speaker 12: hourly wages of its sort of autoworker in Germany, it's 669 00:34:47,760 --> 00:34:50,120 Speaker 12: close to like sixty two euros an hour. You go 670 00:34:50,200 --> 00:34:52,840 Speaker 12: to Hungary just not too far away, it's sixteen euros 671 00:34:52,840 --> 00:34:53,160 Speaker 12: an hour. 672 00:34:54,000 --> 00:34:55,080 Speaker 9: Okay, how do they fix it? 673 00:34:55,160 --> 00:34:57,080 Speaker 6: And that's a silly question, but like do they need 674 00:34:57,160 --> 00:35:00,320 Speaker 6: a massive amount of subsidies to stimulate demand or do 675 00:35:00,400 --> 00:35:02,399 Speaker 6: they need sort of unions to get out of their way. 676 00:35:02,760 --> 00:35:05,240 Speaker 6: What would make this process a little bit easier. 677 00:35:06,000 --> 00:35:07,919 Speaker 12: It's going to be a combination of all of those things. 678 00:35:07,960 --> 00:35:09,680 Speaker 12: And one thing that makes it even harder, Alex if 679 00:35:09,719 --> 00:35:11,400 Speaker 12: we haven't talked about yet, is there actually our new 680 00:35:11,520 --> 00:35:14,000 Speaker 12: EU regulations coming into force at the end of this 681 00:35:14,200 --> 00:35:17,120 Speaker 12: year that basically stipulate that twenty percent of all the 682 00:35:17,200 --> 00:35:19,880 Speaker 12: cars that they need to sell basically need to be EV's. 683 00:35:19,960 --> 00:35:22,560 Speaker 12: The problem is there's a mismatch between what policy makers 684 00:35:22,640 --> 00:35:25,000 Speaker 12: wants and what the market wants. Right now, the market 685 00:35:25,080 --> 00:35:27,880 Speaker 12: is stuck. These guys are still selling below fifteen percent evs. 686 00:35:28,160 --> 00:35:29,920 Speaker 12: If they fail to hit that twenty percent by the 687 00:35:30,000 --> 00:35:32,200 Speaker 12: end of this year, they're looking at potentially billions of 688 00:35:32,239 --> 00:35:34,680 Speaker 12: euros worth of fines. So that's just another sort of 689 00:35:34,800 --> 00:35:36,880 Speaker 12: overlay there. But this all, you know, I mean, the 690 00:35:36,920 --> 00:35:39,279 Speaker 12: policy sort of dissonance that exists is one issue. But 691 00:35:39,360 --> 00:35:41,759 Speaker 12: really these are car makers that have it's partially their 692 00:35:41,800 --> 00:35:43,600 Speaker 12: own doing, right, They were just far behind on this. 693 00:35:43,880 --> 00:35:47,520 Speaker 12: The former Volkswagen CEO he really wanted to lean into electrification. 694 00:35:47,760 --> 00:35:49,359 Speaker 12: They weren't into that. They got rid of him, they 695 00:35:49,400 --> 00:35:51,680 Speaker 12: brought in somebody new, and now they're really paying the price. 696 00:35:52,160 --> 00:35:54,879 Speaker 2: Holl Or, how much of this is nationalism We've seen 697 00:35:54,920 --> 00:35:57,440 Speaker 2: with Apple with the iPhone, maybe the concerns that the 698 00:35:57,520 --> 00:36:00,440 Speaker 2: Chinese consumer on the margin doesn't want to buy Western products. 699 00:36:00,920 --> 00:36:02,239 Speaker 12: So listen, I think that that is going to be 700 00:36:02,320 --> 00:36:04,240 Speaker 12: part of it. And of course this is the Chinese 701 00:36:04,360 --> 00:36:07,360 Speaker 12: policymakers have been trying to gear their sort of economy 702 00:36:07,440 --> 00:36:10,640 Speaker 12: to not have the kind of dependencies that they've historically had. Remember, 703 00:36:10,719 --> 00:36:13,160 Speaker 12: the Chinese were really sort of welcoming with open arms 704 00:36:13,239 --> 00:36:15,680 Speaker 12: Volkswagen back in the nineteen eighties when they first sort 705 00:36:15,680 --> 00:36:18,680 Speaker 12: of started producing cars in the Chinese market, because there 706 00:36:18,800 --> 00:36:21,880 Speaker 12: was no automotive industry in China. What is interesting now 707 00:36:21,920 --> 00:36:23,960 Speaker 12: is you're seeing some of these European car makers now 708 00:36:24,040 --> 00:36:26,680 Speaker 12: sort of inverting that sort of same dynamic, where you 709 00:36:26,760 --> 00:36:30,240 Speaker 12: have partnerships with say Stilantis and Leap Motor, a Chinese 710 00:36:30,560 --> 00:36:33,520 Speaker 12: company have a joint venture here in Europe where now 711 00:36:33,600 --> 00:36:36,120 Speaker 12: Stilandis owns fifty one percent, so that they are starting 712 00:36:36,160 --> 00:36:38,680 Speaker 12: to build Chinese cars at their own plants. And that 713 00:36:38,840 --> 00:36:40,200 Speaker 12: is so we talked about the sort of threat to 714 00:36:40,239 --> 00:36:42,800 Speaker 12: the Chinese market that is also coming very very quickly 715 00:36:43,080 --> 00:36:44,880 Speaker 12: and very soon to the European shores. 716 00:36:44,640 --> 00:36:48,080 Speaker 6: As well, which is so ironic because if Europe really 717 00:36:48,120 --> 00:36:50,480 Speaker 6: wanted to green stuff fast, they would just import a 718 00:36:50,520 --> 00:36:53,719 Speaker 6: boltload of Chinese evs on the cheap and have their 719 00:36:53,760 --> 00:36:55,640 Speaker 6: consumers buy them to your point. Then it becomes like 720 00:36:55,680 --> 00:36:58,320 Speaker 6: a nationalized point. Who's in the worst who's in the 721 00:36:58,400 --> 00:37:00,560 Speaker 6: best spot of this tobackle? 722 00:37:01,320 --> 00:37:02,799 Speaker 12: Have you really really put me on the spot here 723 00:37:02,840 --> 00:37:06,440 Speaker 12: we're try to get for I mean, listen, I'll tell 724 00:37:06,440 --> 00:37:08,279 Speaker 12: you this. The Lantis lost a fifth of their value 725 00:37:08,280 --> 00:37:10,839 Speaker 12: since the profit warding two weeks ago. For thinking about 726 00:37:10,880 --> 00:37:12,879 Speaker 12: companies that are really well positioned in Europe. You think 727 00:37:12,880 --> 00:37:15,680 Speaker 12: about Tesla, They've got manufacturing over in Germany, and guess 728 00:37:15,719 --> 00:37:17,959 Speaker 12: what they make only evis, so they're able to even 729 00:37:18,040 --> 00:37:20,120 Speaker 12: sell some of those credits when those regulations kick in 730 00:37:20,239 --> 00:37:20,600 Speaker 12: next year. 731 00:37:21,080 --> 00:37:23,960 Speaker 4: Our thanks to Oliver Crook, Bloomberg Europe Correspondent. 732 00:37:24,719 --> 00:37:29,239 Speaker 1: This is the Bloomberg Intelligence podcast, available on Apples, Spotify, 733 00:37:29,440 --> 00:37:32,319 Speaker 1: and anywhere else you will get your podcasts. Listen live 734 00:37:32,440 --> 00:37:35,839 Speaker 1: each weekday ten am to noon Eastern on Bloomberg dot Com, 735 00:37:36,120 --> 00:37:39,480 Speaker 1: b iHeartRadio app, tune In, and the Bloomberg Business app. 736 00:37:39,640 --> 00:37:42,640 Speaker 1: You can also watch us live every weekday on YouTube 737 00:37:42,880 --> 00:37:44,640 Speaker 1: and always on the Bloomberg terminal